Money validator

ABSTRACT

A method of classifying a test article as one of a plurality of acceptable denominations of articles of currency, comprising applying a statistical classification process which employs discriminant surfaces derived from previous test articles known to be valid or invalid and is arranged to distinguish therebetween, characterized by applying also an acceptance boundary test which limits the acceptance volume for each denomination so as to exclude forgeries for which the discriminant surfaces were not derived.

BACKGROUND OF THE INVENTION

This invention relates to a money validator for validating articles ofvalue; particularly but not exclusively, to a money validator forvalidating bank notes.

Money validators are known which comprise sensors (e.g. a plurality ofoptical heads) for generating sensed signals in response to an articleto be validated, and decision means (e.g. a microprocessor,microcomputer or LSI circuit) for decided, on the basis of the sensedsignals, whether the article corresponds to a valid denomination or not.Generally, the validation process may consist of comparing the sensedsignals (or values derived therefrom), with predetermined thresholdswhich define acceptable ranges or "windows" of values, within whichsignals from a valid article are assumed to lie and outside whichsignals from invalid articles are likely to lie.

It is known to vary the thresholds, and/or the ranges or windows, overtime to take account of drift, either in the response of the sensors, orin the properties of a population of articles to be validated; see, forexample, European patent 155126 and International application WO80/01963(British Patent 2059129), or European published Application EP-A-0560023and corresponding U.S. application Ser. No. 08/013,708.

This technology is found to be effective where there is a clear limitboundary between a genuine sample population and a false samplepopulation. However, this is not always the case. Separating the genuinefrom the false population, in the measurement space defined by thesensor output signal axes, may require a complex, non-linear boundary.

A pattern recognition technique known in the general field of patternrecognition is the so called "neural network" technique. In neuralnetwork techniques, sensor output signals are supplied to a plurality ofsimilar parallel processing units, the outputs of which comprise somefunction of their inputs. In practice, the "units" are usually notprovided by separate hardware, but by a single processor executingsequential processing.

Neural networks generally operate in two phases. In a training phase,the functions applied by each unit are derived by an interative trainingprocess comprising presenting known genuine and false samples to thenetwork. In the case of a "supervised" type of network, such as the backpropagation or perceptron type, the outputs of the units are monitored,compared with a "correct" network output, and the difference or errorbetween the actual network output and the correct output is propagatedback to incrementally affect the functions applied by each unit to itsinputs. Where two or more network layers are provided, the function is anon-linear (e.g. sigmoidal) function of the weighted sum of the inputs,which enables the network to discriminate between disjoint patterns, andallows complex non-linear pattern separation discriminants.

After the training phase is complete (and, typically, the training phasemay require many hundreds of thousands of iterative adjustments to thenetwork) the network is found to have adapted to the statistics of thepopulation of true and false articles to be validated which waspresented during the training phase, and can generally perform anon-linear (in measurement space) separation between true and falsearticles which are subsequently presented to the network.

Hitherto, however, neural networks have not been applied to banknoterecognition. Indeed, there would be resistance to the use of neuralnetwork techniques in money validation, because it is difficult toaccurately characterise the level of performance of the network since,due to the non-linear nature of the signal processing applied by thenetwork, it is not always clear how effectively, or in what manner, thenetwork is discriminating between true and false articles.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides a money validatorcomprising a classifier which classifies articles as one of a number ofpredetermined types of articles of value utilising classification datacomprising predetermined coefficients derived from a known training setof articles of value to output, at each classification, a plurality ofresults each corresponding to said types, characterised in that, inclassifying the measurements of the test article, the classification isperformed a plurality of times, once for each class of article of valueto be recognised, and in each case the classification is performed totake account of changes in the statistics of the population of oneparticular class of articles of value, the statistical classificationbeing utilised to confirm or otherwise that the test article correspondsto that class. Needless to say, the plural classifications can becarried out in sequence or in parallel.

It might seem surprising that where a plurality of classificationresults are calculated for different classes by the classifier, thisprocess of calculating classification results for a plurality ofdifferent classes should be performed whilst adjusting theclassification process for one particular class. Nonetheless, from thefollowing, it will be understood that this leads to a significantimprovement in the accuracy of the classification.

In the above aspect, the classifier may comprise a neural network; thatis to say, it may comprise a classifier performing a number of parallelcalculations (i.e. calculations on the same input data, whether carriedout at the same time, or in sequence) utilising predetermined dataderived by iterative modification from test articles of value. In thiscase, the normal method of allowing the validator to adapt to changingpopulation statistics would be to continue to train the neural networkin use. However, we have found that this can undesirably lead towardsthe network either training on forgeries, so as to lower its acceptancerate, or alternatively if steps are taken to avoid this by training thenetwork only on selected valid articles of value, the network becomesunrepresentative and will reject acceptable articles of value.Accordingly, we have found it is preferable to provide a separateadjustment for the change in the statistics (e.g. average and/ordeviation therefrom) of populations of each class of articles of valueto be recognised, so as to align their statistics with those for whichthe classifier was derived.

In another aspect, the present invention provides a banknote recognitionapparatus comprising a neural network (as above defined) derived usingthe back propagation method, such as a multilayer perceptron network.This type of network is found to give a surprisingly robust performance,and to provide a remarkably effective transformation of the input data;for example where (as in the below embodiment) the input data comprisesa very large number (e.g. 270) of optical measurements of the testarticle, it has been found possible to achieve recognition using only 12hidden layer nodes within the neural network. Thus, the network haseffectively transformed a 270 dimensional input measurement space into a12 dimensional measurement space in which the different classes (e.g. 72different denominations/orientations) are effectively partitioned.

In another aspect, the present invention consists in providing a neuralnetwork classifier for a banknote validator comprising a plurality ofdifferent neural networks, one for the banknote set of each of aplurality of different countries, or one for a group of countries (e.g.Western Europe).

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be illustrated, by way of example only, withreference to the accompanying drawings, in which:

FIG. 1 shows schematically the structure of a banknote validator withwhich the present invention is suitable for use;

FIG. 2 is a block diagram showing schematically the electricalcomponents of a validator according to FIG. 1;

FIG. 3 is a block diagram showing the functional elements of a validatoraccording to an embodiment of the present invention;

FIG. 4, comprising FIGS. 4a-4d, is a flow diagram showing schematicallythe operation of a validator according to the embodiment of FIG. 3;

FIG. 5 is a block diagram illustratively showing the operationsperformed by a neural network unit forming part of the embodiment ofFIG. 3;

FIG. 6 shows the activation function performed by nodes of the neuralnetwork of FIG. 5;

FIG. 7 is a diagram illustrating, in an exemplary two dimensionedpattern space, the operation of the embodiment of FIG. 3;

FIG. 8 is a block diagram showing in greater detail the structure of astatistical adaption unit forming part of the embodiment of FIG. 3;

FIG. 9 is a flow diagram showing one method of deriving the coefficientsemployed in the embodiment of FIG. 3; and

FIG. 10 is a flow diagram forming part of FIG. 9 and showingillustratively one way of deriving the data characterising a neuralnetwork unit of the kind illustrated in FIG. 5.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to FIG. 1, a banknote validator according to one embodiment ofthe invention comprises a drive system comprising, for example, aplurality of pairs of rollers (for clarity, only one pair of rollers 1a,1b is shown here) for moving a banknote 5 to be validated along a pathP1 beneath a plurality of sensors 2a, 2b, 2c. In this embodiment, thesensors 2a-2c are optical colour measurement heads each arranged todetect optical amplitude at selected, different wavelengths,substantially as described in GB 1470737 and/or DE 2924605. Themeasurement heads 2a-2c are positioned in different lateral positionsacross the banknote 5. As the banknote 5 is moved past the measurementheads 2a-2c by the drive system 1, the output signals, for each sensedoptical wavelength from the measurement heads 2a-2c are supplied in acontinual sequence of samples, to a decision circuit 3 comprising, inthis embodiment, a microcomputer (for example, one of the Motorola 68000series of microcomputers) comprising an input circuit, a processingunit, and memory for storage of programme and constant data and fortemporary storage of processing results and sensor output data.

Controlled by the decision circuit 3 is an accept/reject gate 4 moveablebetween a first position, in which the banknote 5 passes on an acceptpath P2 (to, for example, a cash box) and a reject path P3 on which thenote passes to, for example, an outlet slot for returning the note.

Thus far, the above described apparatus corresponds to known moneyvalidators such as, for example, the BSN 385 money validator availablefrom Mars Electronics International Inc., (Sodeco Cash ManagementSystems Division), Geneva Branch, Chemin Pont-du-Centenaire 109, CH-1228Plan-Les-Ouates, Switzerland, and other constructional details of thedrive system 1, the optical measurement heads 2, the decision circuit 3and the accept/reject gate 4 may be as in that apparatus.

Referring to FIG. 2, each of the measurement heads 2a, 2b, 2c comprisesa plurality of light sources (e.g. LEDs) 20a, 20b, 20c each generating adifferent optical wavelength (the term "optical" is not limited towavelengths visible to the human eye). The sources 20a-20c are energisedin sequence by a multiplexer 22 to illuminate the banknote 5, and thediffuse reflection from the banknote 5 is received by a sensor 21 whichgenerates a corresponding output signal indicative of the diffusereflected intensity. The output of the sensor 21 is sampled in sequencewith the multiplexer 22, to provide a train of analogue signal sampleswhich are output to the decision circuit 3. Within the decision circuit3, the output of the sensor 21 is received at an analogue to digitalconverter 31, which outputs a corresponding train of digital signalsamples to a demultiplexer 32, which demultiplexes the samples andsupplies the three demultiplexed sensor outputs to corresponding buffers33a-33c.

The buffers 33a-33c may, for example be contiguous portions of a singleread/write memory. Each buffer 33a-33c therefore contains a timesequence of output values, corresponding to the diffuse reflectedintensity from a respective one of the optical wavelengths of the LEDs20a-20c, as the banknote 5 is moved past the sensor head 2a. Forexample, each buffer may contain thirty signal values corresponding to ascan of the length of a given banknote type.

The thirty outputs of each buffer (a total of 270 outputs) are suppliedto the decision logic 34, which supplies a banknote identificationsignal identifying which of a plurality of possible valid banknotes hasbeen recognised for utilisation by the apparatus to which the validatoris connected (for example, an automatic ticket dispensing machine) and atrue/false drive signal for driving a gate solenoid 4a forming part ofthe gate 4, to select between the accept and reject positions of thegate 4.

Referring to FIG. 3, the functional elements provided by the decisioncircuit 3 comprise a thresholding unit 35, a flagged candidate buffer36, a measurement correcting unit 37, a neural network classifier unit38, an output logic unit 40, and a statistical adaptation unit 42.

In use, as described in greater detail below, the threshold unit 35receives the measurements from the buffers 33 and subjects them to arelatively coarse test which rejects articles which differ greatly fromany acceptable banknote. Each time the measurements do fall within thethresholds associated with a particular type of acceptable banknote in aparticular type of orientation, a candidate entry flag is stored in theflagged candidate buffer 34 indicating the corresponding type ofbanknote and orientation thereof.

Next, for each flagged candidate type in the flagged candidate buffer36, a statistical correction is applied to the measurements from thebuffers 33, to account for measurement and population drift, using datastored in the statistical adaptation unit 42, a separate correctionbeing applied for each candidate in the buffer 36 corresponding to thedrift for that candidate type.

Then, the corrected measurements are applied to a neural networkclassifier unit 38 in turn. The outputs of the neural network classifier38 comprise one or more recognition signals relating to each type ofbanknote to be recognised.

The output recognition signals from the neural network unit 38 arecombined by the output logic unit 40 to provide a signal identifying aparticular type of recognised banknote, or a signal rejecting thebanknote and actuating the gate 4.

In the event that a particular type of banknote is recognised, thestatistics held in the statistical adaptation unit 42 are updated toreflect the measurements held in the buffer 33 for the just acceptedbanknote.

Referring to FIG. 4, the operation of the decision circuit 3 will not bediscussed in greater detail.

Threshold Unit 35

FIG. 4a indicates the operation of the decision circuit 3 in providingthe threshold circuit 35. The threshold circuit 35 makes use ofstatistical data held in the statistical adaptation circuit 42. Thestatistical adaptation unit holds, for each note to be recognised (forexample eighteen notes), in each orientation (typically, fourorientations) a means and a standard deviation value m_(l),n ; σ_(l),n ;where 1 is the index of the banknote and orientation type (l=1 to L(e.g. 72)) and n is the index of the measurement (n=1 to N, where N isthe total number of measurements (e.g. 270)).

For each measurement, the error between the measurement and thecorresponding value stored for a given candidate is calculated asfollows: ##EQU1##

If this error is greater than a predetermined threshold Q, an errorscore E (initialised at zero) is incremented; if it is equal to or lessthan the threshold the error score E is not incremented. The threshold Qmay conveniently be the same for all measurements, for a given candidatenote/orientation. Typically, the threshold Q is set at around 4(corresponding to 4 standard deviations) for the measurement concerned.

Thus, when all measurements have been thresholded, the incremented totalE corresponds to the number of occasions on which individualmeasurements have exceeded the respective threshold for the candidate.This total E is then compared with a threshold T_(l), and if the errordoes not exceed the threshold then a flag corresponding to the candidateis stored in the flagged candidate buffer 36. If the threshold T_(l) isexceeded, then the candidate is not stored in the flagged candidatebuffer. The threshold T for each candidate is selected to be relativelywide, so that substantially all valid banknotes of the type andorientation concerned will be accepted, regardless of whether someconfusably similar banknotes or forgeries would also be accepted.

The process is repeated for each of the L candidates. After this, ifthere are no flagged candidates in the flagged candidate buffer 36, thebanknote is not even remotely similar to any of the acceptabledenominations, and the control unit 3 actuates the reject gate 4 toreject the banknote, as shown in FIG. 4c.

Values for Q and T for the various denomination/orientations are storedin memory provided in the threshold unit 35.

Correction Unit 37

Referring to FIG. 4b, when functioning as the correction unit 36 thedecision circuit 3 is arranged, for each of the candidatenote/orientations indicated by the contents of the flagged candidatebuffer 34, to read the measurement values from the buffer 33 and tocorrect each for the effects of drift by dividing by the running average(m_(l),n) relevant to that candidate l supplied by the statisticaladaption unit 42.

The magnitude of the measurements are aligned with those for which theneural network unit 38 was trained by multiplying each measurement bythe mean value (m_(l),n), of the measurement in question, which wasencountered in the banknotes used to train the neural network unit 38for that candidate l. Thus, for each measurement, a correctedmeasurement m'_(n) is calculated as follows: ##EQU2##

Neural Network Unit 38

Referring to FIG. 5, the schematic structure of the neural network unit38 is illustrated. The corrected measurements m'₁ -m'_(n) are eachsupplied to each of a plurality of hidden layer nodes 381a-381i (in thisembodiment, twelve such hidden layer nodes 381 are employed), and theoutputs of each of the hidden layer nodes 381a-381i are each supplied toeach of two L output layer nodes (two output layer nodes for each noteto be recognised in each orientation). Output layer nodes 382a-382f areillustrated here.

For each of the L note/orientation combinations, a first output layernode (for example 382a) generates an output signal the value of whichindicates whether the tested note is recognised as corresponding to thatcandidate or not, and a second output layer node (for example 382b)generates an output signal indicating whether the tested notecorresponds to a known forgery for that type of candidate or not.

The weights W of the output nodes are generated in the training phasesuch that the outputs of the output nodes 382 are saturated (i.e. eitherhigh or low).

In this embodiment, the neural network unit 38 provides a multilayerperceptron or back propagation type network (known of itself), in whichthe output Y of each node is given by: ##EQU3## where X represents thevector of inputs to the node, and W_(ij) is a corresponding vector ofpredetermined weighting coefficients derived during the training phaseand stored by the neural network unit 38. As shown in FIG. 5, one of theinputs to each node maybe a constant (e.g. H as shown); the other inputsare, in the case of the hidden layer nodes 381a, the vector ofmeasurements m'₁ -m'_(n) and, in the case of the output layer nodes 382,the vector of outputs of the hidden layer nodes 381.

The function S is a non-linear function which acts to compress the rangeof the node output signal; preferably it has a generally sigmoidal shapeas illustrated in FIG. 6. In this embodiment, it is given by ahyperbolic tangent: ##EQU4##

In this embodiment, the neural network unit 38 may store multiple neuralnetworks of the type shown in FIG. 5; one neural network being providedfor the note set of each country in which the validator might be used,and the neural network unit being operable to selectively employ onlyone of the neural networks.

It will readily be seen that the hidden layer nodes 381 and output layernodes 382 need not be provided by separate processors but could be (andin this embodiment are) provided by a microcomputer circuit arranged tocalculate and store the output of each hidden layer node in turn, andthen use these to calculate the output of each output layer in turn, inaccordance with the above relationships.

The weight values W for each node 381a-381c, 382a-382f of the hiddenlayer and the output layer are accordingly stored in memory providedwithin the neural network unit 38, and thus effectively characterise thenetwork.

Operation of the Neural Network Unit 38

Referring to FIG. 4b, for each candidate l in the flagged candidatebuffer 36, the correction unit 37 reads the running average (m_(l),n)and the average of the training data m_(l),n for each measurement valueheld in the buffer 33, and calculates corrected measurements m'_(n) asdiscussed above. These corrected measurements are then employed asinputs to the neural network unit 38, which calculates output values foreach of the two L outputs nodes 382a-382f. The output values are thentemporarily retained. Next, the correction unit proceeds to the nextcandidate in the flagged candidate buffer 36, reads the running averageand training data averages from the statistical adaptation unit, readsonce more the measurements from the buffer 33, and calculates a new setof corrected measurements, corrected for the drift which would beencountered for that candidate. This corrected set of measurementsm'_(n) are then presented to the neural network unit 38 which once morecalculates the outputs of the output layer nodes 382a-382f, and oncemore buffers these.

This process is then repeated for each of the candidates in the flaggedcandidate buffer 36.

Output Logic Unit 40

The output logic unit 40 receives the 2L outputs of the neural networkunit 38 for each of the candidates in the flagged candidate buffer. Onthe basis of these output node outputs, the output logic unit 40generates either a recognition output indicating recognition of one ofthe valid banknotes, or a signal indicating that no valid banknote hasbeen recognised (to operate the reject gate 4).

For each of the possible candidates in the flagged candidate buffer 36,the outputs of the output nodes 382 may be in one of several conditions:

1. The "true" output node for the candidate being considered may be theonly output node which is generating a positive output signal.

2. A different output node may be generating a positive output signal.

3. Several output nodes may be generating a positive output signal.

4. One of the "false" output nodes may be generating a positive outputsignal, indicating recognition of a forgery.

5. No output node may be generating a high output signal (in which case,the document is completely unknown to a neural network 38).

In all cases other than the first of the above, the output logic unit 40generates a signal to reject the tested note. Thus, a note is rejectedeven if there is confusion as to its identity (indicated by severaloutputs being high) or its identity is false (indicated by a high outputother than at the output node which corresponds to the candidate inquestion for which the input measurements were corrected).

Furthermore, the note is also rejected if confusion has arisen because asingle (apparently correct) high output signal has been generated formore than one of the candidates in the flagged candidate buffer 36; thatis to say when correcting the data for a first flagged candidate givesrise to an apparently correct result but so does correcting the data fora second flagged candidate.

In the case where there is no confusion, either between different outputnodes for the same corrected input data or for network outputs fordifferently corrected data, the single correct output node output signalidentifies the note value and orientation, which are output by theoutput logic unit for use by the automatic machinery with which thevalidator is to be used.

Effect of the Threshold Unit 35

Referring to FIG. 7, in a two dimensional illustration of a measurementspace, the measurement values for genuine notes of a first denominationare clustered in a group N1; those for a second denomination in a groupN2 and those for a third denomination in a group N3. A forgery of notesof the first type is shown clustered as F1; two forgeries for a note ofthe second type are shown as clusters F2; and a forgery for a note ofthe third type is shown as a cluster F3.

The boundaries corresponding to the transition between different outputnodes 382a-382f are shown as thin lines A. It will be seen that theneural network unit 38 has found lines or surfaces which can effectivelydiscriminate between each genuine note and the known forgeries. However,these separation planes leave a large volume of space (for example thespace above the cluster N1 or that to the left of the cluster N3)unpartitioned, because no forgeries having measurements corresponding tothese regions were present in the data on which the neural network wastrained. Accordingly, although the neural network unit 38 is accurate indiscriminating between different types of genuine note, and betweengenuine notes and known forgeries, it gives a poor, or at best, anunknown performance against new types of forgeries having measurementsfalling in such volumes of space.

Referring once more to FIG. 7, however, the threshold unit 35 acts toapply a relatively wide acceptance threshold around each genuinebanknote cluster N1, N2, N3, and thus effectively limits the volume inthe measurement space within which test articles will be accepted ascorresponding to each of those banknote classes. Accordingly, althoughthe threshold unit 35 is completely ineffective in discriminatingbetween the banknotes N1 and N2 and their known forgeries F1 and F2, itoffers a considerable measure of protection of the validator againstunknown forgeries whereas the neural network unit 38 provides a muchmore accurate discrimination against known forgeries.

Statistical Adaptation Unit 42

Referring to FIG. 8, the statistical adaptation unit 42 comprises aread/write store 421 (e.g. an area of RAM) and an arithmetic processingunit 422 arranged to read and write to the store 421, and to performpredetermined statistical processing on the contents thereof.

As shown in FIG. 8, the store 421 stores N×L records (where N is thenumber of measurements and L is the number of denominations/orientationcandidate notes). Each record comprises three numerical values; arunning average (m_(l),n) a running deviation σ_(l),n, and a trainingset mean m_(l),n. The last of these is a constant, the value of which isnot altered by the processor 422.

When the output logic unit 40 indicates, as described above, that avalid note of a particular denomination in a particular orientation hasbeen recognised, the candidate number l of the note is supplied to thestatistical processor 422. The statistical processor 422 then reads eachof the N running average and deviation values in the table 421 relevantto that candidate l and calculates an updated average and deviationvalue utilising the existing value and the corresponding measurementvalue read from the buffer 33. The updated value is then stored in thestore 421 for subsequent use.

In this embodiment, the running average (m_(l),n) is a running mean, andthe deviation σ_(l),n is the standard deviation. Accordingly, in thisembodiment, the statistical processor 422 is arranged to calculate thenew mean value as ##EQU5## and the standard deviation is ##EQU6##

The values of the parameters P₁ P₂ determine the influence of each newmeasurement on the running average and deviation, and are thereforerelatively large in practice. The values P₁ P₂ may, in anotherembodiment, be functions of the number of times the stored values havebeen adjusted; in this case, for each candidate a count is incrementedeach time an adjustment is made to the mean and deviation values forthat candidate and the P₁ P₂ values are determined in accordance on thecount (for example by a linear relationship P_(i) =A_(i) +B_(i) ·count).

Thus, each time a banknote is accepted, the average (and in thisembodiment, the deviation) of the measurements found for the recognisedclass of banknote are updated to take account of drift of thecharacteristics of the banknote or the validator.

Training the Neural Network 38

Although it is not necessary for an appreciation of the operation of theinvention, a brief description of the method of training the neuralnetwork unit 38 so as to derive suitable values for the weightingcoefficients W, will now be disclosed. Specific details of the method inso far as it applies to back propagation MLP networks as described inthe above embodiments, are well known to the skilled person and aredescribed, for example in:

Rumelhart D E, Hinton G E and Williams R J: `Learning internalrepresentation by error propagation`, in `Parallel DistributedProcessing: Explorations in the Microstructures of Cognition`, edsRumelhart and McClelland, 1, Foundations, pp 318-362, MIT Press (1986)(incorporated herein by reference),

or in

"Introduction aux reseaux de neurones"

F. Blayo

Cours du departement d informatique, laboratoire de microinformatiqueEPFL.

pp. 52-58

Lausanne, 25 mars 1991, or

"Une procedure d apprentissage pour reseau a seuil asymetrique"

Y. Le Cun

Actes de COGNITIVA 85, pp. 522-604

Paris, juin 1985, or (in general terms) in

"Adaptive Switching Circuits"

B. Window, M. E. Hoff 1960 IRE WESCON Convention Record, New York: IRE,pp. 96-104, 1960.

A measurement system similar to (but not necessarily implemented by) themeasurement heads 2a, 2b, 2c measures the sensor values from a pluralityof banknotes comprising a training set. The training set comprisessubsets of each banknote in each orientation, and of all known forgeries(if any) for each note/orientation.

Referring to FIG. 9, each subset of the training set is randomlyselected to comprise, for example, 10% of a larger available populationof banknotes.

Next, the neural network is trained as shown in FIG. 10 (i.e. the valuesof the weighting constants are derived) by initially selecting a randomset of weighting constant values; then calculating, using theseweighting constant values, what the outputs of the hidden layer381a-381i and output neurons 382a-382f would be; then comparing theseoutputs with the "correct" outputs for the first node (i.e. +1 for theoutput node 382a382f corresponding to the identity of the note, and -1for all other output nodes); and then adjusting the weights of eachoutput layer node and hidden layer node using the back propagationmethod to incrementally shift the calculated outputs corresponding tothe output layer nodes towards the "true" values.

This is then repeated for each node of the training set, in randomorder, and the process is iterated until the outputs calculated tocorrespond to the output layer nodes give a very high rejection rate(i.e. closely approximating 100%) for forgeries, and a high acceptancerate (i.e. above 97%) for all genuine banknotedenominations/orientations.

If the weighting values give rise to output node output values whichmeet these criteria, the weighting values are considered to havesuccessfully characterised the neural network (i.e. the neural networkhas been trained successfully), and the weighting values are stored.

Returning to FIG. 9, the next step is to use all the measurement valuesfor all the notes of each subset to calculate the training data mean andthe initial value of the standard deviation, to be stored in the table421. The value of the running means is, at this stage, set to the samevalue as the training set means.

Next, the thresholds Q utilised by the threshold unit 35 are set forexample at 4σ, in other words 4 times the standard deviation for eachmeasurement derived from the training data.

The error score total T is then calculated so as to obtain 100%acceptance of all the banknote denominations/orientations in the set.

Finally, the performance of the validator is emulated using the neuralnetwork weighting coefficients; the thresholds Q; means, standarddeviations and global error T calculated as above, and if all correctnotes are accepted and all forgeries are rejected, the values thuscalculated are taken to be sufficiently accurate. If this test is notmet, another training set of notes is selected in the process of FIG. 9is begun again.

When an accepted set of weight coefficients W thresholds Q and so onhave been found, they are stored in the store 41, the threshold unit 35and the neural network unit 38 of an unprogrammed validator which isotherwise as described above, and which is then ready for use.

It will be clear that the above described process may be performed oncomputing apparatus of any suitable kind arranged to receive measurementsignals or previously stored data corresponding to measurement signals,from banknotes. As a validator itself comprises a measurement unittogether with, in some embodiments, a microprocessor, the validator maybe programmed to perform the training process just described itselfprior to use in validation, but the manner in which the datacharacterising the validator whilst validating is derived is notrelevant to the operation of the validator whilst validating.

Other Embodiments

In a further embodiment, the structure of the neural network shown inFIG. 5 is simplified so that the true/false output nodes 382b, 382d,382f are omitted and only a single output node for eachdenomination/orientation is present. This reduces the volume ofcalculation to be performed by the network.

Although in the above description the mean and standard deviation arediscussed, it would equally be possible to employ different measures ofthe running average and variability of the data; for example bymaintaining a tracking median and/or a measure based on the sum ofdifferences from the running average. In some embodiments, it may bepossible merely to adapt either the running average or the measure ofvariability, but not both.

Although in the above described embodiments, the threshold unit 35applies a coarse test comprising applying a plurality of thresholds andthen counting the number of deviations from the thresholds, other coarseacceptance tests are also possible; for instance, deriving a "cityblock" distance metric or a Mahalanobis distance metric. However, insome aspects it is preferred to utilise coarse tests which share withthe above described embodiment the feature of accepting, in a largenumber of measurements, a small number of deviations from acceptablelimits due to (for example) a small damaged area of a note.

In the foregoing, the "neural network" when used to validate will beunderstood to comprise apparatus which calculates, utilisingpredetermined weighting values, a number of outputs from input data asdescribed above, whether or not such calculations are performed inparallel and regardless of whether they are performed by dedicatedhardware or a general purpose calculating device suitably programmed.The weighting values, as described above, are found iteratively fromtraining data.

In another embodiment, the validator itself is arranged to perform theabove training operations, for example to allow learning of new banknotetypes in the field.

Although in the above described embodiments, each measurement isnormalised prior to application to the neural network, it will beunderstood that the same effect could be achieved by temporarilynormalising the coefficient values of the input nodes 381 of the neuralnetwork, and this is equally within the scope of the invention. Equally,throughout the foregoing description, the skilled reader will understandthat (except at nonlinear stages of the process) the associative anddistributive laws of arithmetic are applicable so that operations may beperformed in other orders or manners than those described above, toachieve the same effect.

Although LED's have been mentioned, it will be clear that other types ofoptical emitter could be employed, operating in the visible or invisiblespectra. Equally, magnetic or other sensors could be employed instead.

Further, other types of nonlinear function could easily be employed; forexample; a Radial Basis function, or a sigmoid functionS(u)=1/(I+e^(-u)).

Accordingly, many other modifications may be made to the precedinginvention without departing from its scope and nature.

I claim:
 1. A method of classifying a test article as one of a pluralityof acceptable denominations of articles of currency, comprising:derivinga plurality of measurement signals from a test article; and applying anacceptance process to said measurement signals, said acceptance processcalculating first, second and third outputs indicating correspondencewith first, second and third denominations, wherein for each said testitem said acceptance process is performed a plurality of times,differently adjusted to compensate for the effects of measurement driftfor at least said first, second and third denominations at eachperformance, and in that in each performance of said acceptance process,an output corresponding to the denomination for which the acceptanceprocess is adjusted is calculated, and at least two other outputs fortwo different denominations are also calculated.
 2. A method accordingto claim 1 in which said acceptance process is performed said pluralityof times in succession.
 3. A method according to claim 1 in which saidacceptance process is adjusted by processing said measurements using,for each denomination, stored drift data, to produce correctedmeasurements for each denomination, and applying a common acceptanceprocess to said corrected measurements.
 4. A method according to claim1, further comprising applying an acceptance boundary test which limitsthe acceptance volume for each denomination so as to exclude forgeriesnot corresponding to training articles from which said process wasderived.
 5. A method according to claim 4, in which the acceptanceboundary test comprises:applying, for each acceptable denomination,relatively relaxed acceptance criteria so as to reject thosedenominations for which said relaxed acceptance criteria are not metprior to applying said acceptance process.
 6. A method according toclaim 1, in which the acceptance process comprises forming, for eachacceptable denomination, a linear function of said measurement signals;and applying a continuous non-linear function to the result of saidlinear function.
 7. A method according to claim 1, in which theacceptance process comprises a neural network process.
 8. A methodaccording to claim 7, in which the neural network process comprises afeed-forward process, the constants characterising the process havingbeen iteratively derived in a training phase by back propagation.
 9. Amethod according to claim 7, in which the neural network processcomprises two network layers.
 10. A method according to claim 9 in whichthe hidden layer of said neural network comprises substantially fewernodes than the inputs thereto.
 11. A money validator arranged to operatethe method of claim
 1. 12. A method according to claim 1, comprisingemploying optical sensing of the article of value.
 13. A methodaccording to claim 12, in which the optical sensing is performed at aplurality of different wavelengths.
 14. A method of classifying a testarticle as one of a plurality of acceptable denominations of articles ofcurrency, comprising:applying a classification process employingpreviously derived and stored reference data of valid or invalidtraining article examples of the denominations and arranged todistinguish therebetween, wherein level data relating to the range oraverage level of measurement values derived from the training articlesis stored, for each of the denominations; processing the measurementsderived from test articles using the stored level data to align themwith each said stored range for each denomination during theclassification process; and forming, for each denomination, a correctionfactor responsive to the ratio between said level data and a runningaverage of said measurements.
 15. A method according to claim 14, inwhich said level data is stored as an average value for each saiddenomination.
 16. A method according to claim 14 further comprisingapplying an acceptance boundary test which limits the acceptance volumefor each denomination so as to exclude forgeries not corresponding totraining articles from which said process was derived.
 17. A methodaccording to claim 16 in which the acceptance boundary testcomprisesapplying, for each acceptable denomination, relatively relaxedacceptance criteria so as to reject those denominations for which saidrelaxed acceptance criteria are not met prior to applying saidacceptance process.
 18. A method according to claim 14 in which theacceptance process comprises forming, for each acceptable denomination,a linear function of said measurement signals; and applying a continuousnon-linear function to the result of said linear function.
 19. A methodaccording to claim 14, in which the acceptance process comprises aneural network process.
 20. A method according to claim 19 in which theneural network process comprises a feed-forward process, the constantscharacterising the process having been iteratively derived in trainingphase by back propagation.
 21. A method according to claim 19 in whichthe neural network process comprises two network layers.
 22. A methodaccording to claim 21 in which the hidden layer of said neural networkcomprises substantially fewer nodes than the inputs thereto.
 23. A moneyvalidator arranged to operate the method of claim
 14. 24. A methodaccording to claim 14 comprising employing optical sensing of thebanknote or article of value.
 25. A method according to claim 24 inwhich the optical sensing is performed at a plurality of differentwavelengths.
 26. A money validator for classifying a test article as oneof a plurality of acceptable denominations of articles of currency,comprising:sensor means for deriving a plurality of measurements fromsaid test article; non-linear discriminant means for deriving aplurality of different linear combinations of said measurements and thenapplying, to each, a respective non-linear compression function (e.g. asigmoid), to generate a plurality of non-linear output signals; decisionmeans for rejecting a test article or accepting said test article as oneof said plurality of denominations, based on said non-linear outputs;and adjustment means for applying a plurality of different adjustments,corresponding to said plurality of denominations, to said measurementsto produce a plurality of sets of adjusted measurements, each of whichis separately supplied to said means for producing a plurality of linearcombinations.
 27. A money validator for classifying a test article asone of a plurality of acceptable denominations of articles of currency,comprising:sensor means for deriving a plurality of measurements fromthe test article defining a point in measurement space; non-lineardiscriminant means for providing a plurality of discriminants whichpartition said measurement space into at least one acceptance regioncorresponding to at least one of said plurality of acceptabledenominations, by deriving a plurality of different linear combinationsof the measurements and then applying, to each, a respective non-linearcompression function (e.g. a sigmoid), to generate a plurality ofnon-linear output signals; means for applying an additional acceptanceboundary test which limits the volume of the acceptance region in saidmeasurement space which would otherwise be applied by said validator;and decision means for rejecting a test article or accepting the testarticle as one of said plurality of denominations, based on thenon-linear output signals in dependence on whether the point lies withinthe acceptance region.
 28. A method for classifying a test article asone of a plurality of acceptable denominations of articles of currency,comprising:deriving a plurality of measurements from the test articledefining a point in measurement space; providing a plurality ofdiscriminants which partition said measurement space into at least oneacceptance region corresponding to at least one of said plurality ofacceptable denominations, by deriving a plurality of different linearcombinations of the measurements and then applying, to each, arespective non-linear compression function (e.g. a sigmoid), to generatea plurality of non-linear output signals; applying an additionalacceptance boundary test which limits the volume of the acceptanceregion in said measurement space which would otherwise be applied bysaid validator; and rejecting a test article or accepting the testarticle as one of said plurality of denominations, based on thenon-linear output signals in dependence on whether the point lies withinthe acceptance region.